Abnormal dynamic community structure of the salience network in depression

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Abnormal interactions in resting-state functional networks are observed in depression.
• The dynamic interplay between brain networks in depression remains poorly understood.

Purpose of the Study:

• To investigate temporal variations in internetwork interactions to uncover dysfunction in depressive disorders.
• To explore the dynamics of crucial brain network interactions and their relation to depression.

Main Methods:

• Acquired resting-state functional magnetic resonance imaging (fMRI) data from 20 depressed patients and 20 healthy controls.
• Extracted 12 resting-state networks using group-independent component analysis.
• Analyzed network interactions with a sliding windowed Granger causality model and detected modular structures using multislice community detection.

Main Results:

• No significant differences in overall modularity or module numbers between groups.
• Depressed patients showed significantly decreased flexibility of the salience network (SN) compared to controls (P=0.048).

Conclusions:

• The salience network (SN) in depression exhibits reduced participation in functional modules and less frequent changes in its network allegiance.
• These findings highlight the potential role of the SN in the neuropathological mechanisms underlying depression.